A browsing system for a database using visualization of user preferences

Kanai, Hideaki; Hakozaki, Katsuya

doi:10.1109/iv.2000.859768

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…A third common approach, especially with visualizations that adopt a visual form showing networks or clusters where the position is flexible, uses the positioning of elements to emphasize a recommendation. For example, work from Kanai and Hakozaki [37] as well as Leuski and Allan [46] use positional shifts to move recommended items "close" to the user in 3D visualizations. More recently, Nazemi et al [52] followed a similar philosophy to visualize document relationships.…”

Section: Forcefulnessmentioning

confidence: 99%

“…The largest group of paper in this category respond indirectly to user interactions [14,27,37,42,44,72]. For example, this category includes systems that update recommended content in response to user actions such as a change to visualization settings, selecting or inspecting visualized objects, or moving and resizing items.…”

Section: Stabilitymentioning

confidence: 99%

“…The final type of triggering event we observed is data entry [37]. Kanai and Hakozaki describe an approach to the content recommendation which maintains a model of user preferences to guide the identifi-cation of related content.…”

Section: Stabilitymentioning

confidence: 99%

See 2 more Smart Citations

A Design Space for Surfacing Content Recommendations in Visual Analytic Platforms

Zhou¹,

Wang²,

Guo³

et al. 2022

Preprint

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Recommendation algorithms have been leveraged in various ways within visualization systems to assist users as they perform of a range of information tasks. One common focus for these techniques has been the recommendation of content, rather than visual form, as a means to assist users in the identification of information that is relevant to their task context. A wide variety of techniques have been proposed to address this general problem, with a range of design choices in how these solutions surface relevant information to users. This paper reviews the state-of-the-art in how visualization systems surface recommended content to users during users' visual analysis; introduces a four-dimensional design space for visual content recommendation based on a characterization of prior work; and discusses key observations regarding common patterns and future research opportunities.

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Section: Forcefulnessmentioning

confidence: 99%

Section: Stabilitymentioning

confidence: 99%

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A Design Space for Surfacing Content Recommendations in Visual Analytic Platforms

Zhou¹,

Wang²,

Guo³

et al. 2022

Preprint

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“…Hybrid systems try to integrate Content-Based Filtering (CBF) technique (e.g., [6,13]), which uses the features or content of the item for prediction, into CF technique to improve recommendation quality. The recommendation quality of hybrid systems are improved significantly compared with the former CF based systems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Recommender System Using Latent Features

Maneeroj

Takasu

2009

2009 International Conference on Advanced Information Networking and Applications Workshops

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This paper proposes a hybrid recommender system that utilizes latent features. The main problem discussed in this paper is the cold start problem. To handle this problem, the proposed system first extracts latent features from items represented by a multi-attributed record using a probabilistic model. Then, it calculates the similarity of users from their ratings. Both similarities between items and users are used for predicting unknown rating of a user to a item. We evaluate the proposed method using a movie data set and shows that the proposed method achieves good performance for small ratings information.

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